Probabilistic opinion pooling aims to aggregate the probabilistic beliefs of multiple agents to reach a consensus. When dealing with high uncertainty contexts, agents’ beliefs are often represented by imprecise probabilities, i.e. intervals of probability values. The most commonly used aggregation method for imprecise opinion pooling is linear pooling, which takes a weighted average of the input opinions. However, determining an optimal weight distribution for pooling is a complex challenge. In this work, we propose a novel elicitation method inspired by epistemic voting that provides probabilistic guarantees for agents to hold a correct belief. Furthermore, we show how to derive well-performing pooling weights from the elicited beliefs using existing results for the voting rule on which our elicitation method is based. Finally, we carry out parametric simulations that illustrate the whole process of elicitation and weighting and that show an increase in the quality of the aggregated opinions.